Abstract:
This study presents the design of a multi-epitope vaccine against HIV-1 using bioinformatics and artificial intelligence (AI) approaches to accelerate vaccine development and reduce the time and cost compared to traditional methods.
Methods:
An immunoinformatics approach was employed to select potent and specific epitopes from the HIV-1 envelope protein gp120. The vaccine construct includes 5 B-cell epitopes, 8 cytotoxic T-lymphocyte (CTL) epitopes, and 12 helper T-lymphocyte (HTL) epitopes. These epitopes were linked using appropriate linkers and an adjuvant was added to enhance immunogenicity. The physicochemical properties of the vaccine construct were evaluated. Secondary and tertiary structure prediction, molecular docking with the TLR3 receptor, and 100 ns molecular dynamics (MD) simulations were performed to assess stability. In addition, C-IMMSIM immune simulation was used to predict the in vivo immune response.
Biography :
Analytical, organized Lab Manager with 12 years in leading team to develop Quality work and efficient results in laboratory setting. Highly organized and diligent with systematic approach to handling specimens and conducting assessments. Streamlined lab processes and worked with other departments to prioritize tasks with strongest effect on Work environment.
